The motivation behind fusing multimodality, multiresolution images is to create a single image with improved interpretability. In this paper, we propose a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) for spatially registered, multi-sensor, multi-resolution medical images. RT is a new Multi-scale Geometric Analysis (MGA) tool, capable of resolving two dimensional (2D) singularities and representing image edges more efficiently. The source medical images are first transformed by discrete RT (DRT). Different fusion rules are applied to the different subbands of the transformed images. Then inverse DRT (IDRT) is applied to the fused coefficients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis shows, that the proposed technique performs better compared to fusion scheme based on Contourlet Transform (CNT).
Malay Kumar Kundu,
"Medical Image Fusion Based on Ripplet Transform Type-I," Progress In Electromagnetics Research B,
Vol. 30, 355-370, 2011. doi:10.2528/PIERB11040601
1. Daneshvar, S. and H. Ghassemian, "MRI and PET image fusion by combining IHS and retina-inspired models," Information Fusion, Vol. 11, No. 2, 114-123, April 20 2010. doi:10.1016/j.inffus.2009.05.003
2. Barra, V. and J. Y. Boire, "A general framework for the fusion of anatomical and functional medical images," NeuroImage, Vol. 13, No. 3, 410-424, March 2001. doi:10.1006/nimg.2000.0707
3. Shivappa, S. T., B. D. Rao, and M. M. Trivedi, "An iterative decoding algorithm for fusion of multimodal information," EURASIP Journal on Advances in Signal Processing, Vol. 2008, 2008.
4. Li, S. and B. Yang, "Multifocus image fusion using region segmentation and spatial frequency," Proceedings of Image Vision Computing, Vol. 26, No. 7, 971-979, July 2008. doi:10.1016/j.imavis.2007.10.012
5. Yonghong, J., "Fusion of landsat TM and SAR image based on principal component analysis," Remote Sensing Technology and Application, Vol. 13, No. 1, 46-49, March 1998.
6. Li, H., B. S. Manjunath, and S. K. Mitra, "Multisensor image fusion using the wavelet transform," Proceedings of CVGIP: Graphical Model and Image Processing, Vol. 57, No. 3, 235-245, May 1995. doi:10.1006/gmip.1995.1022
7. Yang, Y., D. S. Park, S. Huang, and N. Rao, "Medical image fusion via an effective wavelet-based approach," EURASIP Journal on Advances in Signal Processing, Vol. 2010, 2010.
8. Amolins, K., Y. Zhang, and P. Dare, "Wavelet based image fusion techniques | An introduction, review and comparison," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 62, No. 4, 249-263, Sept. 2007. doi:10.1016/j.isprsjprs.2007.05.009
9. Yanga, L., B. L. Guoa, and W. Ni, "Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform," Neurocomputing, Vol. 72, No. 1--3, 203-211, Dec. 2008. doi:10.1016/j.neucom.2008.02.025
10. Ali, F. E., I. M. El-Dokany, A. A. Saad, and F. E. Abd El-Samie, "Curvelet fusion of MR and CT images," Progress In Electromagnetics Research C, Vol. 3, 215-224, 2008. doi:10.2528/PIERC08041305
11. Xu, J., L. Yang, and D. Wu, "Ripplet: A new transform for image processing," Journal of Visual Communication and Image Representation , Vol. 21, No. 7, 627-639, Oct. 2010. doi:10.1016/j.jvcir.2010.04.002
12. Starck, J. L., E. J. Candes, and D. L. Donoho, "The curvelet transform for image denoising," IEEE Transactions on Image Processing, Vol. 11, No. 6, 670-684, Jun. 2000. doi:10.1109/TIP.2002.1014998